the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Aug 2025
Solar Backscatter Ultraviolet (BUV) Retrievals of Mid-Stratospheric Aerosols from the 2022 Hunga Eruption
Abstract. On January 15, 2022, a highly explosive eruption of the submarine Hunga volcano (Kingdom of Tonga) generated the largest stratospheric hydration event ever observed and the largest aerosol perturbation since the 1991 Pinatubo eruption. Here, we develop a novel method for satellite retrieval of stratospheric aerosol optical depth (AOD) and layer peak height (Zp) using solar backscattered ultraviolet (BUV) radiation; this is made possible by the exceptional mid-stratospheric altitude of the Hunga aerosols. We analyze BUV observations of the Hunga stratospheric aerosol cloud on January 17, 2022 (47 hours after the eruption), using UV band 1 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on board the ESA/Copernicus Sentinel-5 precursor (S5P) satellite and the Ozone Mapping and Profiling Suite- Nadir Profiler (OMPS-NP) on board the NOAA-20 satellite. We retrieve AOD and Zp by fitting hyperspectral BUV radiance ratios in a narrow spectral window restricted to 289–296 nm, chosen in order to reduce interference from tropospheric clouds while highly sensitive to stratospheric aerosols located above ozone maximum altitude. The retrieval employs radiative transfer calculations from the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) forward model. We assume a single Hunga aerosol layer composed of polydisperse sulfuric acid spherical particles embedded in a Rayleigh atmosphere with a known ozone profile. The ozone profile is supplied from a version of the MERRA-2 Stratospheric Composition Reanalysis of the Microwave Limb Sounder (MLS) on board NASA EOS Aura satellite — produced by NASA's Global Modeling and Assimilation Office using a stratospheric chemistry model and MERRA-2 meteorology. We also include a dynamic SO2 layer, which coincides spatially with the retrieved aerosol vertical profile, and with the total loading normalized to the stratospheric SO2 vertical column density from the operational TROPOMI SO2 product. We validate our AOD retrievals against ground-based AERONET direct-sun AOD measurements as well as co-located OMPS-NP retrievals, and Zp retrievals against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses using Lagrangian trajectory modeling. We estimate the total Hunga stratospheric “wet” aerosol mass to be Maer ~ 0.5±0.05 Tg. This value is consistent with our previous BUV estimates of Hunga gaseous sulfur dioxide (SO2) emissions ( ~0.5 Tg SO2), and with the rapid conversion of SO2 to sulfuric acid (sulfate) aerosol during the initial plume dispersion (SO2 e-folding time ~ 6 days), and ~0.5 acid mass fraction in aqueous sulfuric acid solution.
Competing interests: Some authors are members of the editorial board of AMT
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-2938', Bernard Legras, 12 Sep 2025
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RC2: 'Comment on egusphere-2025-2938', Anonymous Referee #1, 13 Sep 2025
This is an excellent and comprehensive paper that shows that stratospheric aerosol parameters can be retrieved from nadir backscatter measurements for a large volcanic plume that is above the ozone layer. The paper is well written, and the problem is approached systematically with detailed explanations and appropriate appendices. The paper is highly appropriate for the scope of AMT and needs very little revision for publication. A few minor comments are included below for the authors to address in a revision.
The definition of wet aerosol mass (in the abstract) should be clarified (and why is “wet” in quotations?)
Section 3.1 Normalization to background radiances includes a correction for ozone profile differences but it is highly feasible that air density differences are not characterized by M2-SCREAM in the early plume. What is the potential impact?
Section 3.4 Description of the unimodal particle size distribution parameters does not make sense. What is the sensitivity to the choice of 8 discrete ordinates with delta M scaling?
Section 3.5 No results or analyses are shown from the validation with synthetic data. Typically a retrieval result and comparison with the input state would be shown just to verify the algorithm fidelity.
Section 4.2 The retrieved AOD varies greatly with the refractive index assumptions. Is this wide range of refractive indices realistic for Hunga?
Citation: https://doi.org/10.5194/egusphere-2025-2938-RC2
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- 1
This is a very thorough and careful study of the retrieval of aerosol properties in the early stratospheric plume that followed the Hunga eruption on 15 January 2022. It contains a number of technical aspects (this is OK in AMT) which are usually well explained and several appendices in support (that I confess I did not check). I like a lot many points of the discussions. It should be published but I have a number of mostly minor comments and suggestions for improvements that I would like the authors to address before. They are listed in order of appearance and not of importance. All references, those already included and those not, are listed at the end for convenience
Figure 1: Please mention the time for each swath and track shown in this figure. It is quite important to account that they are not simultaneous and that the plume was moving quite fast at about 20 angular degrees on the average. This is discussed around Figure 7 but it should be mentioned earlier.
Longitudes are missing on figure 1. Why is the location of Lucinda indicated as approximate?
In general, please consider making fonts for labels larger on all figures. AMT uses a two-column layout and some axes may become hardly readable if reduced in size.
This figure shows that there were two components in the plume, which are also seen in Carn el al. (2022) and labelled C1 (western cloud) and C2 (eastern cloud) in Legras et al. (2022), with different altitude and composition and which evolved in a different way over the first weeks after the eruption until they mixed. Unfortunately, this difference is not clearly conveyed in the analysis and the discussion. The C1 cloud is aerosol rich because it is also water rich and the conversion from SO2 to sulfate occurred faster than in C2. It also descended faster because of stronger water radiative cooling.
It is important to mention in section 2 or 3 that the injected water vapour had strong local effects on the temperature of the stratosphere that were not represented correctly by standard assimilation systems which did not assimilate water vapour data. After a while the assimilation of temperature data recovers correct profiles even if the budget was wrong (cf Coy et al., 2022) but large errors occurred during the first days. This is also because the GPS radio-occultation signal was strongly contaminated by the unusual high level of water in the stratosphere (Randel et al., 2023). Some data were rejected but not all. Since M2-SCREAM assimilates water from MLS, it has largely corrected these effects and exhibits an equilibrated heat budget (Coy et al., 2022). However, I would not be confident that everything was perfectly right over the first few days after the eruption and I recommend to check the temperature profiles from high-quality radiosoundings available from Australian stations (Vömel et al., 2023). During that period the two plumes descended very fast, especially C1, owing to the cooling by water vapour (Sellitto et al., 2022).
L272-275: These details can be left for the appendix. Once Jacobians have been mentioned, it is clear that linearization is required.
Section 3.3: The iterative procedure is described but the first guess is also important as the minimization path can lead to spurious minima. It is not indicated how this problem is avoided. Perhaps it does not occur in this application but often look-up tables are needed to provide a good enough first guess.
L330: I would say the cloud is more centered at 30 km than at 32 km. A single daily crossing of C1 is here used but there are other relevant CALIOP data for the same day that should be used, especially because they are on night orbits with much better S/N. More precisely, there is a crossing of C2 at 16:19 UTC (orbit 2022-01-17T15-45-52ZN) and a crossing of C1 at 17:57 UTC (orbit 2022-01-17T17-24-22ZN). Of course, the signal is strong enough to see the plume clearly on daily data but night data allow to check the low value of depolarization which supports spherical liquid sulfates and the absence of solid particles. The large signal and the isolation of the plume layer leads also to a determination of the AOD (Duchamp et al., 2025) that could be used in section 4.2.
L.341. There is a known thermodynamic dependency of wt on temperature and humidity for which a suitable parameterization is available in Tabazadeh et al. (1997). This might significantly restrain the range of values to be considered and perhaps give a way to a more reliable estimate than what is done in section 4.4.
L348: Sg = 1.545 µm does not make sense for a standard deviation (especially with a median at 0.14 µm) but 1.545 (without dimension) makes perfectly sense as the width parameter of the lognormal distribution. I made a check myself using the Lucinda v2.0 data for 17 January 2022 and I found a better fit to the volume size distribution with a median at 0.13 µm and a width parameter 1.57 but the retained values are probably OK. By the way, Boichu et al. (2023) give 0.22-0.23 µm as the effective radius (I find 0.21 µm) but I do not see where the width 1.545 is mentioned in this paper. They use v1.5 version of the data.
L355 A more up to date source for high-resolution ozone cross-sections with temperature dependency is Serdyunchenko et al. (2011).
L395 In the range of altitudes and latitude of the plume the horizontal shear was more significant than the vertical shear. This is visible in the fast deformation of the clouds between images collected on 16 January (e.g. Carn et al., 2022) and the present study.
L.404 It is a bit difficult to appreciate the altitude by eye since the colour scale is very smooth in this range of altitudes. Is this estimate of 31 km produced by a spatial average of the data or is it a visual estimate? An other estimate should be given for the C2 cloud and please also exploit the day orbit for C1 in the same way. The clouds are not uniform and the sampling of CALIOP is on a very narrow curtain.
L405 Of course, 1 km over 30 km is 3% but what is the range of retrievable altitudes for TROPOMI and the width of the kernels which would provide a better idea of the accuracy.
Section 4.4: ). Carn et al. (2022) estimated a SO2 e-folding time of τ=6 days based on satellite estimates of the SO2 column and used this to estimate a total emitted mass of about 0.45 Tg of SO2 from the 15 January eruption. However, this analysis did not separate the C1 and C2 clouds, and the lifetime of SO2 in C1 was likely shorter, which suggests an underestimation of the total injected mass reported by Carn et al. (2022). Even if a new estimate is not done here, this issue might be considered.
L535: I give little trust to this estimate of wt by such indirect way which is subject to large uncertainties (see above). I would prefer an estimate based on thermodynamic data as mentioned above.
Bruckert et al. (2025) is now published. Do not forget to update the reference.
References:
Boichu, M., Grandin, R., Blarel, L., Torres, B., Derimian, Y., Goloub, P., Brogniez, C., Chiapello, I., Dubovik, O., Mathurin, T., Pascal, N., Patou, M., and Riedi, J.: Growth and Global Persistence of Stratospheric Sulfate Aerosols From the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruption, JGR Atmospheres, 128, e2023JD039010, https://doi.org/10.1029/2023JD039010, 2023.
Bruckert, J., Chopra, S., Siddans, R., Wedler, C., and Hoshyaripour, G. A.: Aerosol dynamic processes in the Hunga plume in January 2022: does water vapor accelerate aerosol aging?, Atmospheric Chemistry and Physics, 25, 9859–9884, https://doi.org/10.5194/acp-25-9859-2025, 2025.
Carn, S. A., Krotkov, N. A., Fisher, B. L., and Li, C.: Out of the blue: Volcanic SO2 emissions during the 2021–2022 eruptions of Hunga Tonga—Hunga Ha’apai (Tonga), Front. Earth Sci., 10, 976962, https://doi.org/10.3389/feart.2022.976962, 2022.
Coy, L., Newman, P. A., Wargan, K., Partyka, G., Strahan, S. E., and Pawson, S.: Stratospheric Circulation Changes Associated With the Hunga Tonga‐Hunga Ha’apai Eruption, Geophysical Research Letters, 49, e2022GL100982, https://doi.org/10.1029/2022GL100982, 2022.
Duchamp, C., Legras, B., Podglajen, A., Sellitto, P., Bourassa, A. E., Rozanov, A., Taha, G., and Zawada, D. J.: Aerosol Composition and Extinction of the 2022 Hunga Plume Using CALIOP, https://doi.org/10.5194/egusphere-2025-3355, 16 July 2025.
Legras, B., Duchamp, C., Sellitto, P., Podglajen, A., Carboni, E., Siddans, R., Grooß, J.-U., Khaykin, S., and Ploeger, F.: The evolution and dynamics of the Hunga Tonga–Hunga Ha’apai sulfate aerosol plume in the stratosphere, Atmos. Chem. Phys., 22, 14957–14970, https://doi.org/10.5194/acp-22-14957-2022, 2022.
Randel, W. J., Johnston, B. R., Braun, J. J., Sokolovskiy, S., Vömel, H., Podglajen, A., and Legras, B.: Stratospheric Water Vapor from the Hunga Tonga–Hunga Ha’apai Volcanic Eruption Deduced from COSMIC-2 Radio Occultation, Remote Sensing, 15, 2167, https://doi.org/10.3390/rs15082167, 2023.
Sellitto, P., Podglajen, A., Belhadji, R., Boichu, M., Carboni, E., Cuesta, J., Duchamp, C., Kloss, C., Siddans, R., Bègue, N., Blarel, L., Jegou, F., Khaykin, S., Renard, J.-B., and Legras, B.: The unexpected radiative impact of the Hunga Tonga eruption of 15th January 2022, Commun Earth Environ, 3, 288, https://doi.org/10.1038/s43247-022-00618-z, 2022.
Serdyuchenko, A.: New broadband high-resolution ozone absorption cross-sections, 23, 2011.
Tabazadeh, A., Toon, O. B., Clegg, S. L., and Hamill, P.: A new parameterization of H2SO4/H2O aerosol composition: Atmospheric implications, Geophysical Research Letters, 24, 1931–1934, https://doi.org/10.1029/97GL01879, 1997.
Vömel, H., Evan, S., and Tully, M.: Water vapor injection into the stratosphere by Hunga Tonga-Hunga Ha’apai, Science, 377, 1444–1447, https://doi.org/10.1126/science.abq2299, 2022.